Clarifying hierarchical age–period–cohort models: A rejoinder to Bell and Jones

Abstract

Previously, Reither et al. (2015) demonstrated that hierarchical age–period–cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide “misleading evidence dressed up as science.” Despite such strong words, B&J show no curiosity about their own simulated data—and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the “true” DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy—testing HAPC models with data simulated from contrived DGPs that violate important assumptions—is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences.

Age, Period, and Cohort Differences in Work Centrality and Work Values

“…They also show empirically that the HAPC model works in "real-life situations" where the researchers do not suppose perfectly linear cohort and period effects. In another paper, Reither, Land, et al [5]-demonstrating the consensus among APC scholars with an expanded list of coauthors-conclude that APC models fail only in the presence of exact algebraic effects of temporal variables, but in other specifications, APC models (including HAPC) are appropriate.…”

“…To illustrate our point: while usually in the literature the political/economic/technological periodization of generations follows the dominant trends in the United States 7 , Diepstraten, Ester, and Vinken [22] identify "prewar", "silent", "protest", "lost" and "pragmatic" generations for the Netherlands on the basis of an entirely different national "story". 5 The bibliographical references of the quotation were omitted. 6 It is assumed that the level of sensitivity towards such events is the highest during childhood and adolescence and that their impact remains relatively stable from then on [21].…”

“…Most of 1 Societies 2018, 8,11 2 of 33 the previous papers on work-related values use research design and/or data that make it difficult to detect age, period, and cohort differences at a population (or national) level. We use hierarchical age-period-cohort (HAPC) models [3,4] that are a suitable solution for the analysis of individual level data of repeated cross-sectional surveys [5]. We are not aware of previous research applying HAPC models to work values.…”

The hierarchical age–period–cohort model: Why does it find the results that it finds?

“…When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al in Soc Sci Med 145:125–128, 2015a ). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.…”

Desigualdade de Rendimentos do Trabalho no Curto e no Longo Prazo: Tendências de Idade, Período e Coorte

“…É necessário proceder testes para avaliar se todas as dimensões IPC são empiricamente distinguíveis (Reither et al, 2015a(Reither et al, e 2015bBell e Jones, 2015 …”